This dissertation proposal examines how teacher participation in networked learning communities relates to levels of teaching innovation. The study will survey teachers participating in the Schools of the Future project in Hawaii. It aims to determine if factors like participation level, new professional relationships, or professional development opportunities correlate with higher levels of innovative teaching practices. The proposal provides background on 21st century skills and professional learning communities, and describes the research questions, instruments, procedures, and analysis plan for the quantitative study.
Talk about how other experts are part of this community Conferenc
KING and NEWMANN found that teachers are most likely to learn when they collaborate with colleagues both within and outsideof their schools and then they access external researchers and program developes
Difficult to research since the important work of the network happens is in the hard to define space between the network and the school (Little, 2005) Important work of the network is the place where relationships are built, transfer of practice and creation of new resources (Little, 2005) Participant characteristics including their backgrounds, why they participate in networks, extent of involvement and participant perspectives of network benefits (Kerr, 2003) – Giant review of 2550 references culled down to 359 references found that we need more research on participant characteristics Caldwell
My graph – will explain it…
We need to run Cronbach’s alpha to test for internal consistency for the custom questions – we are only using for this research –not intending it to be the gold standard – should be greater than 7. In correlational research, we do not (or at least try not to) influence any variables but only measure them and look for relations (correlations) between some set of variables, A correlational analysis allows the researcher to determine the “extent to which variations in one factor correspond with variations in one or more other factors” allowing for the exploration of the interrelationships between several variables simultaneously - how close the relationships follows a straight line (between -1 and +1) T-test The t -test is the most commonly used method to evaluate the differences in means between two groups. For example, the t -test can be used to test for a difference in test scores between a group of patients who were given a drug and a control group who received a placebo. Theoretically, the t-test can be used even if the sample sizes are very small (e.g., as small as 10; some researchers claim that even smaller n 's are possible), as long as the variables are normally distributed within each group and the variation of scores in the two groups is not reliably different DEPENDENT VARIABLE NEEDS TO BE OVER A 5 POINT SCALE TO DO THIS A correlational matrix will be produced, using Pearson’s r-coefficient to test for significance, to test for potential associations between several study variables. Simple Linear Correlation (Pearson r). Pearson correlation (hereafter called correlation ), assumes that the two variables are measured on at least interval scales (see Elementary Concepts ), and it determines the extent to which values of the two variables are "proportional" to each other. The value of correlation (i.e., correlation coefficient) does not depend on the specific measurement units used; for example, the correlation between height and weight will be identical regardless of whether inches and pounds , or centimeters and kilograms are used as measurement units. Proportional means linearly related ; that is, the correlation is high if it can be "summarized" by a straight line (sloped upwards or downwards). Pearson Chi-square. The Pearson Chi-square is the most common test for significance of the relationship between categorical variables. This measure is based on the fact that we can compute the expected frequencies in a two-way table (i.e., frequencies that we would expect if there was no relationship between the variables). For example, suppose we ask 20 males and 20 females to choose between two brands of soda pop (brands A and B ). If there is no relationship between preference and gender, then we would expect about an equal number of choices of brand A and brand B for each sex. The Chi-square test becomes increasingly significant as the numbers deviate further from this expected pattern; that is, the more this pattern of choices for males and females differs
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